View
221
Download
2
Tags:
Embed Size (px)
Citation preview
Understanding Proteomics Understanding Proteomics through Bioinformaticsthrough Bioinformatics
Chris EveloBiGCaT Bioinformatics Group – BMT-TU/e & UMMasterclass Nutrigenomics; May 11 2004
BiGCaT Bioinformatics, BiGCaT Bioinformatics, bridge between two universitiesbridge between two universities
Universiteit MaastrichtPatients, Experiments,
Arrays and Loads of Data
TU/eIdeas & Experience in Data Handling
BiGCaT
BiGCaT Bioinformatics,BiGCaT Bioinformatics,between two research fieldsbetween two research fields
CardiovascularResearch
Nutritional &Environmental
Research
BiGCaT
If transcriptomics is:If transcriptomics is:
The study of genome wide geneexpression on the transcriptional level
Where genome wide means: >20K genes. And transcriptional level means that somehow
>20K mRNA sequences have to be analyzed And >20K expression values have to be
filtered, normalized, replicate treated,clustered and understood
Thus no transcriptomics without bioinformatics
Gene expression Gene expression arraysarraysMicroarrays: relative
fluorescense signals. Identification.
Macroarrays: absolute radioactive signal. Validation.
Then proteomics would be:Then proteomics would be:
The study of genome wide gene expression on the translational level
Where genome wide would mean: >20K proteins.
Then proteomics does not yet exist!
Does it already need bioinformatics?
Identification of proteins found Identification of proteins found (method annotation)(method annotation)
Antibody techniques: build in.You know what the antigen is or you wouldn’t use it.
Mass identification:Fragment libraries derived from SwissprotNot normally a user (scientist) problem.Or practically build in as well.
No current need for bioinformaticsBut please use Swissprot ID’s!!
Data filtering and normalizationData filtering and normalization
Appears to become a problem on antibody arrays (see yesterdays presentation by Rachelle van Haaften).
Start with expertise from mRNA microarrays.
Use bioinformatics to improve techniquesNot to cover up problems
2
time
Exp
r. le
vel
Clustering: find proteins with same expression patterns
T1 signal
T2
sig
na
l
Left hand picture shows expression patterns for 2 proteins (these should probably end up in the same cluster).
Right hand picture shows the expression vector for one protein for the first 2 dimensions. Can be normalized by amplitude (circle) or relatively (square).
Clustering and grouping of Clustering and grouping of proteins with parallel expressions proteins with parallel expressions
Fancy techniques clustering, principal component analysis, self organizing maps, etc. etc.
But… Only useful for high numbers (and maybe not even then)
Currently not important for proteomicsBut might be useful in combined mRNA/proteinstudies
Two things left Two things left
Functional understanding of proteomics results Understanding protein modifications
Functional understanding Functional understanding
Map changed proteins (quantitatively or qualitatively) to known pathways.
Or use information from the Gene Ontology (GO) database
Steal and smartly adapt a transcriptomics tool:GenMapp/Mappfinder
Let me show you an example from a simple nutrigenomics (starvation) study.
Data from Johan Renes.
Understanding protein Understanding protein modificationsmodifications
Map changed proteins (quantitatively or qualitatively) to known pathways.
Or use information from the Gene Ontology (GO) database
Steal and smartly adapt a transcriptomics tool:GenMapp/Mappfinder
Let me show you an example from a simple nutrigenomics (starvation) study.
Data from Johan Renes.
Protein variants derived from single genes
Phosphorylation?Modification?
Alternative splicing?Phosphorylation?
Alternative splicing?Modification?
Understanding modificationsUnderstanding modifications
Look up the protein in SwissProtFor instance:– Glyceraldehyde 3-phosphate dehydrogenase – Pyruvate kinase (note splice variants)
Or use Prosite Search For instance:– Glyceraldehyde 3-phosphate dehydrogenase
with: PKC phosphorylation siteand: its own GAPDH pattern
Bioinformatics helps to see the possibilities